{"id":"https://openalex.org/W4200335485","doi":"https://doi.org/10.1109/gcce53005.2021.9621830","title":"Design of a Fuzzy Inference Based Robot Vision for CNN Training Image Acquisition","display_name":"Design of a Fuzzy Inference Based Robot Vision for CNN Training Image Acquisition","publication_year":2021,"publication_date":"2021-10-12","ids":{"openalex":"https://openalex.org/W4200335485","doi":"https://doi.org/10.1109/gcce53005.2021.9621830"},"language":"en","primary_location":{"id":"doi:10.1109/gcce53005.2021.9621830","is_oa":false,"landing_page_url":"https://doi.org/10.1109/gcce53005.2021.9621830","pdf_url":null,"source":{"id":"https://openalex.org/S4363607807","display_name":"2021 IEEE 10th Global Conference on Consumer Electronics (GCCE)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE 10th Global Conference on Consumer Electronics (GCCE)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5079980907","display_name":"Chihiro Yukawa","orcid":null},"institutions":[{"id":"https://openalex.org/I136446963","display_name":"Okayama University of Science","ror":"https://ror.org/05aevyc10","country_code":"JP","type":"education","lineage":["https://openalex.org/I136446963"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Chihiro Yukawa","raw_affiliation_strings":["Okayama University of Science (OUS), Okayama, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Okayama University of Science (OUS), Okayama, Japan","institution_ids":["https://openalex.org/I136446963"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5086581662","display_name":"Tetsuya Oda","orcid":"https://orcid.org/0000-0001-6306-1976"},"institutions":[{"id":"https://openalex.org/I136446963","display_name":"Okayama University of Science","ror":"https://ror.org/05aevyc10","country_code":"JP","type":"education","lineage":["https://openalex.org/I136446963"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Tetsuya Oda","raw_affiliation_strings":["Okayama University of Science (OUS), Okayama, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Okayama University of Science (OUS), Okayama, Japan","institution_ids":["https://openalex.org/I136446963"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5047682854","display_name":"Nobuki Saito","orcid":null},"institutions":[{"id":"https://openalex.org/I136446963","display_name":"Okayama University of Science","ror":"https://ror.org/05aevyc10","country_code":"JP","type":"education","lineage":["https://openalex.org/I136446963"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Nobuki Saito","raw_affiliation_strings":["Graduate School of Engineering, Okayama University of Science (OUS), Okayama, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Graduate School of Engineering, Okayama University of Science (OUS), Okayama, Japan","institution_ids":["https://openalex.org/I136446963"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5074224869","display_name":"Aoto Hirata","orcid":null},"institutions":[{"id":"https://openalex.org/I136446963","display_name":"Okayama University of Science","ror":"https://ror.org/05aevyc10","country_code":"JP","type":"education","lineage":["https://openalex.org/I136446963"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Aoto Hirata","raw_affiliation_strings":["Graduate School of Engineering, Okayama University of Science (OUS), Okayama, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Graduate School of Engineering, Okayama University of Science (OUS), Okayama, Japan","institution_ids":["https://openalex.org/I136446963"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5005811679","display_name":"Kyohei Toyoshima","orcid":null},"institutions":[{"id":"https://openalex.org/I136446963","display_name":"Okayama University of Science","ror":"https://ror.org/05aevyc10","country_code":"JP","type":"education","lineage":["https://openalex.org/I136446963"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Kyohei Toyoshima","raw_affiliation_strings":["Okayama University of Science (OUS), Okayama, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Okayama University of Science (OUS), Okayama, Japan","institution_ids":["https://openalex.org/I136446963"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5111731834","display_name":"Kengo Katayama","orcid":null},"institutions":[{"id":"https://openalex.org/I136446963","display_name":"Okayama University of Science","ror":"https://ror.org/05aevyc10","country_code":"JP","type":"education","lineage":["https://openalex.org/I136446963"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Kengo Katayama","raw_affiliation_strings":["Okayama University of Science (OUS), Okayama, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Okayama University of Science (OUS), Okayama, Japan","institution_ids":["https://openalex.org/I136446963"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I136446963"],"apc_list":null,"apc_paid":null,"fwci":5.516,"has_fulltext":false,"cited_by_count":15,"citation_normalized_percentile":{"value":0.97109171,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":91,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"806","last_page":"807"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10763","display_name":"Digital Transformation in Industry","score":0.996999979019165,"subfield":{"id":"https://openalex.org/subfields/2209","display_name":"Industrial and Manufacturing Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10763","display_name":"Digital Transformation in Industry","score":0.996999979019165,"subfield":{"id":"https://openalex.org/subfields/2209","display_name":"Industrial and Manufacturing Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12111","display_name":"Industrial Vision Systems and Defect Detection","score":0.9757999777793884,"subfield":{"id":"https://openalex.org/subfields/2209","display_name":"Industrial and Manufacturing Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11159","display_name":"Manufacturing Process and Optimization","score":0.9394999742507935,"subfield":{"id":"https://openalex.org/subfields/2209","display_name":"Industrial and Manufacturing Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7994475364685059},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.708294153213501},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6872478127479553},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.6799511909484863},{"id":"https://openalex.org/keywords/robot","display_name":"Robot","score":0.5318916440010071},{"id":"https://openalex.org/keywords/machine-vision","display_name":"Machine vision","score":0.518527626991272},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.4840223491191864},{"id":"https://openalex.org/keywords/servomotor","display_name":"Servomotor","score":0.47706520557403564},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.455293208360672}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7994475364685059},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.708294153213501},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6872478127479553},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.6799511909484863},{"id":"https://openalex.org/C90509273","wikidata":"https://www.wikidata.org/wiki/Q11012","display_name":"Robot","level":2,"score":0.5318916440010071},{"id":"https://openalex.org/C5339829","wikidata":"https://www.wikidata.org/wiki/Q1425977","display_name":"Machine vision","level":2,"score":0.518527626991272},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.4840223491191864},{"id":"https://openalex.org/C109441226","wikidata":"https://www.wikidata.org/wiki/Q1137255","display_name":"Servomotor","level":2,"score":0.47706520557403564},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.455293208360672},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/gcce53005.2021.9621830","is_oa":false,"landing_page_url":"https://doi.org/10.1109/gcce53005.2021.9621830","pdf_url":null,"source":{"id":"https://openalex.org/S4363607807","display_name":"2021 IEEE 10th Global Conference on Consumer Electronics (GCCE)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE 10th Global Conference on Consumer Electronics (GCCE)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Industry, innovation and infrastructure","score":0.6399999856948853,"id":"https://metadata.un.org/sdg/9"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":2,"referenced_works":["https://openalex.org/W2888648656","https://openalex.org/W2907071956"],"related_works":["https://openalex.org/W3082583057","https://openalex.org/W1941135743","https://openalex.org/W4388250257","https://openalex.org/W2317985886","https://openalex.org/W4388248724","https://openalex.org/W2981382809","https://openalex.org/W2055243143","https://openalex.org/W4321487865","https://openalex.org/W4313906399","https://openalex.org/W373159082"],"abstract_inverted_index":{"With":[0],"the":[1,14,19,36,79,92,97,106,109,113,119],"promotion":[2],"of":[3,38,49,59,83,108],"Industry":[4],"4.0,":[5],"artificial":[6],"intelligence":[7],"has":[8],"recently":[9],"been":[10],"attracting":[11],"attention":[12],"in":[13,112],"manufacturing":[15],"industry.":[16],"In":[17,66,95],"particular,":[18],"image":[20,39,64,81,86,120],"recognition":[21,40],"such":[22,41],"as":[23,42,51],"Convolutional":[24],"Neural":[25],"Network":[26],"(CNN)":[27],"are":[28],"using":[29,45],"active":[30],"inspection":[31],"and":[32,54,61],"testing":[33],"processes.":[34],"However,":[35],"learning":[37],"CNN":[43,84],"is":[44,100],"a":[46,57,71,88],"large":[47],"amount":[48],"images":[50],"training":[52,85],"data":[53],"it":[55],"takes":[56],"lot":[58],"time":[60],"effort":[62],"to":[63,91,104,116],"acquisition.":[65],"this":[67],"paper,":[68],"we":[69],"propose":[70],"fuzzy":[72,102],"inference":[73,103],"based":[74],"robot":[75,93,114],"vision":[76],"system":[77,99],"for":[78],"automatic":[80],"acquisition":[82,121],"from":[87],"camera":[89],"mounted":[90],"arm.":[94],"addition,":[96],"proposed":[98],"considering":[101],"suppress":[105],"vibration":[107],"servo":[110],"motor":[111],"arm":[115],"speed":[117],"up":[118],"process.":[122]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":5},{"year":2022,"cited_by_count":6}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}